18.175: Lecture 23
Random walks
Scott Sheffield
MIT
18.175 Lecture 23
Outline
Random walks
Stopping times
Arcsin law, other SRW stories
Outline
Random walks
Stopping times
Arcsin law, other SRW stories
Exchangeable events
I
Start with measure space (S, S, µ). Let
Ω = {(ω1 , ω2 , . . .) : ωi ∈ S}, let F be product σ-algebra and
P the product probability measure.
18.175 Lecture 23
Exchangeable events
I
Start with measure space (S, S, µ). Let
Ω = {(ω1 , ω2 , . . .) : ωi ∈ S}, let F be product σ-algebra and
P the product probability measure.
I
Finite permutation of N is one-to-one map from N to itself
that fixes all but finitely many points.
18.175 Lecture 23
Exchangeable events
I
Start with measure space (S, S, µ). Let
Ω = {(ω1 , ω2 , . . .) : ωi ∈ S}, let F be product σ-algebra and
P the product probability measure.
I
Finite permutation of N is one-to-one map from N to itself
that fixes all but finitely many points.
I
Event A ∈ F is permutable if it is invariant under any finite
permutation of the ωi .
18.175 Lecture 23
Exchangeable events
I
Start with measure space (S, S, µ). Let
Ω = {(ω1 , ω2 , . . .) : ωi ∈ S}, let F be product σ-algebra and
P the product probability measure.
I
Finite permutation of N is one-to-one map from N to itself
that fixes all but finitely many points.
I
Event A ∈ F is permutable if it is invariant under any finite
permutation of the ωi .
I
Let E be the σ-field of permutable events.
18.175 Lecture 23
Exchangeable events
I
Start with measure space (S, S, µ). Let
Ω = {(ω1 , ω2 , . . .) : ωi ∈ S}, let F be product σ-algebra and
P the product probability measure.
I
Finite permutation of N is one-to-one map from N to itself
that fixes all but finitely many points.
I
Event A ∈ F is permutable if it is invariant under any finite
permutation of the ωi .
I
Let E be the σ-field of permutable events.
I
This is related to the tail σ-algebra we introduced earlier in
the course. Bigger or smaller?
18.175 Lecture 23
Hewitt-Savage 0-1 law
I
If X1 , X2 , . . . are i.i.d. and A ∈ A then P(A) ∈ {0, 1}.
Hewitt-Savage 0-1 law
I
If X1 , X2 , . . . are i.i.d. and A ∈ A then P(A) ∈ {0, 1}.
I
Idea of proof: Try to show A is independent of itself, i.e.,
that P(A) = P(A ∩ A) = P(A)P(A). Start with measure
theoretic fact that we can approximate A by a set An in
σ-algebra generated by X1 , . . . Xn , so that symmetric
difference of A and An has very small probability. Note that
An is independent of event A0n that An holds when X1 , . . . , Xn
and Xn1 , . . . , X2n are swapped. Symmetric difference between
A and A0n is also small, so A is independent of itself up to this
small error. Then make error arbitrarily small.
18.175 Lecture 23
Application of Hewitt-Savage:
I
If Xi are i.i.d. in Rn then Sn =
Rn .
18.175 Lecture 23
Pn
i=1 Xi
is a random walk on
Application of Hewitt-Savage:
I
I
P
If Xi are i.i.d. in Rn then Sn = ni=1 Xi is a random walk on
Rn .
Theorem: if Sn is a random walk on R then one of the
following occurs with probability one:
18.175 Lecture 23
Application of Hewitt-Savage:
I
I
P
If Xi are i.i.d. in Rn then Sn = ni=1 Xi is a random walk on
Rn .
Theorem: if Sn is a random walk on R then one of the
following occurs with probability one:
I
Sn = 0 for all n
18.175 Lecture 23
Application of Hewitt-Savage:
I
I
P
If Xi are i.i.d. in Rn then Sn = ni=1 Xi is a random walk on
Rn .
Theorem: if Sn is a random walk on R then one of the
following occurs with probability one:
I
I
Sn = 0 for all n
Sn → ∞
18.175 Lecture 23
Application of Hewitt-Savage:
I
I
P
If Xi are i.i.d. in Rn then Sn = ni=1 Xi is a random walk on
Rn .
Theorem: if Sn is a random walk on R then one of the
following occurs with probability one:
I
I
I
Sn = 0 for all n
Sn → ∞
Sn → −∞
18.175 Lecture 23
Application of Hewitt-Savage:
I
I
P
If Xi are i.i.d. in Rn then Sn = ni=1 Xi is a random walk on
Rn .
Theorem: if Sn is a random walk on R then one of the
following occurs with probability one:
I
I
I
I
Sn = 0 for all n
Sn → ∞
Sn → −∞
−∞ = lim inf Sn < lim sup Sn = ∞
18.175 Lecture 23
Application of Hewitt-Savage:
I
I
P
If Xi are i.i.d. in Rn then Sn = ni=1 Xi is a random walk on
Rn .
Theorem: if Sn is a random walk on R then one of the
following occurs with probability one:
I
I
I
I
I
Sn = 0 for all n
Sn → ∞
Sn → −∞
−∞ = lim inf Sn < lim sup Sn = ∞
Idea of proof: Hewitt-Savage implies the lim sup Sn and
lim inf Sn are almost sure constants in [−∞, ∞]. Note that if
X1 is not a.s. constant, then both values would depend on X1
if they were not in ±∞
18.175 Lecture 23
Outline
Random walks
Stopping times
Arcsin law, other SRW stories
Outline
Random walks
Stopping times
Arcsin law, other SRW stories
Stopping time definition
I
Say that T is a stopping time if the event that T = n is in
Fn for i ≤ n.
Stopping time definition
I
Say that T is a stopping time if the event that T = n is in
Fn for i ≤ n.
I
In finance applications, T might be the time one sells a stock.
Then this states that the decision to sell at time n depends
only on prices up to time n, not on (as yet unknown) future
prices.
Stopping time examples
I
Let A1 , . . . be i.i.d. random variables equal to −1 with
probability
P .5 and 1 with probability .5 and let X0 = 0 and
Xn = ni=1 Ai for n ≥ 0.
18.175 Lecture 23
Stopping time examples
I
I
Let A1 , . . . be i.i.d. random variables equal to −1 with
probability
P .5 and 1 with probability .5 and let X0 = 0 and
Xn = ni=1 Ai for n ≥ 0.
Which of the following is a stopping time?
The smallest T for which |XT | = 50
The smallest T for which XT ∈ {−10, 100}
The smallest T for which XT = 0.
The T at which the Xn sequence achieves the value 17 for the
9th time.
5. The value of T ∈ {0, 1, 2, . . . , 100} for which XT is largest.
6. The largest T ∈ {0, 1, 2, . . . , 100} for which XT = 0.
1.
2.
3.
4.
18.175 Lecture 23
Stopping time examples
I
I
Let A1 , . . . be i.i.d. random variables equal to −1 with
probability
P .5 and 1 with probability .5 and let X0 = 0 and
Xn = ni=1 Ai for n ≥ 0.
Which of the following is a stopping time?
The smallest T for which |XT | = 50
The smallest T for which XT ∈ {−10, 100}
The smallest T for which XT = 0.
The T at which the Xn sequence achieves the value 17 for the
9th time.
5. The value of T ∈ {0, 1, 2, . . . , 100} for which XT is largest.
6. The largest T ∈ {0, 1, 2, . . . , 100} for which XT = 0.
1.
2.
3.
4.
18.175 Lecture 23
Stopping time examples
I
I
Let A1 , . . . be i.i.d. random variables equal to −1 with
probability
P .5 and 1 with probability .5 and let X0 = 0 and
Xn = ni=1 Ai for n ≥ 0.
Which of the following is a stopping time?
The smallest T for which |XT | = 50
The smallest T for which XT ∈ {−10, 100}
The smallest T for which XT = 0.
The T at which the Xn sequence achieves the value 17 for the
9th time.
5. The value of T ∈ {0, 1, 2, . . . , 100} for which XT is largest.
6. The largest T ∈ {0, 1, 2, . . . , 100} for which XT = 0.
1.
2.
3.
4.
I
Answer: first four, not last two.
18.175 Lecture 23
Stopping time theorems
I
Theorem: Let X1 , X2 , . . . be i.i.d. and N a stopping time with
N < ∞.
18.175 Lecture 23
Stopping time theorems
I
Theorem: Let X1 , X2 , . . . be i.i.d. and N a stopping time with
N < ∞.
I
Conditioned on stopping time N < ∞, conditional law of
{XN+n , n ≥ 1} is independent of Fn and has same law as
original sequence.
18.175 Lecture 23
Stopping time theorems
I
Theorem: Let X1 , X2 , . . . be i.i.d. and N a stopping time with
N < ∞.
I
Conditioned on stopping time N < ∞, conditional law of
{XN+n , n ≥ 1} is independent of Fn and has same law as
original sequence.
I
Wald’s equation: Let Xi be i.i.d. with E |Xi | < ∞. If N is a
stopping time with EN < ∞ then ESN = EX1 EN.
18.175 Lecture 23
Stopping time theorems
I
Theorem: Let X1 , X2 , . . . be i.i.d. and N a stopping time with
N < ∞.
I
Conditioned on stopping time N < ∞, conditional law of
{XN+n , n ≥ 1} is independent of Fn and has same law as
original sequence.
I
Wald’s equation: Let Xi be i.i.d. with E |Xi | < ∞. If N is a
stopping time with EN < ∞ then ESN = EX1 EN.
I
Wald’s second equation: Let Xi be i.i.d. with E |Xi | = 0 and
EXi2 = σ 2 < ∞. If N is a stopping time with EN < ∞ then
ESN = σ 2 EN.
18.175 Lecture 23
Wald applications to SRW
I
S0 = a ∈ Z and at each time step Sj independently changes
by ±1 according to a fair coin toss. Fix A ∈ Z and let
N = inf{k : Sk ∈ {0, A}. What is ESN ?
18.175 Lecture 23
Wald applications to SRW
I
S0 = a ∈ Z and at each time step Sj independently changes
by ±1 according to a fair coin toss. Fix A ∈ Z and let
N = inf{k : Sk ∈ {0, A}. What is ESN ?
I
What is EN?
18.175 Lecture 23
Outline
Random walks
Stopping times
Arcsin law, other SRW stories
Outline
Random walks
Stopping times
Arcsin law, other SRW stories
Reflection principle
I
How many walks from (0, x) to (n, y ) that don’t cross the
horizontal axis?
Reflection principle
I
How many walks from (0, x) to (n, y ) that don’t cross the
horizontal axis?
I
Try counting walks that do cross by giving bijection to walks
from (0, −x) to (n, y ).
Ballot Theorem
I
Suppose that in election candidate A gets α votes and B gets
β < α votes. What’s probability that A is a head throughout
the counting?
18.175 Lecture 23
Ballot Theorem
I
Suppose that in election candidate A gets α votes and B gets
β < α votes. What’s probability that A is a head throughout
the counting?
I
Answer: (α − β)/(α + β). Can be proved using reflection
principle.
18.175 Lecture 23
Arcsin theorem
I
Theorem for last hitting time.
18.175 Lecture 23
Arcsin theorem
I
Theorem for last hitting time.
I
Theorem for amount of positive positive time.
18.175 Lecture 23